code: def handle_importance(preprocess, df, target):
      number = preprocess["importance"]
  
      if not isinstance(number, int):
          raise Exception(
              "Please provide the number of columns you'd like to remove based on importance"
          )
  
      forest = RandomForestRegressor()
  
      y = df[target]
      del df[target]
  
      forest.fit(df, y)
      importance = forest.feature_importances_
  
      columns = []
  
      if number < 0:
          lowest_indices = importance.argsort()[: number * -1]
  
          for index, column in enumerate(df.columns):
              columns.append(column)
              if index in lowest_indices:
                  del df[column]
      else:
          top_indices = np.argpartition(importance, number * -1)[
              number * 1 :
          ]
  
          for index, column in enumerate(df.columns):
              columns.append(column)
              if index not in top_indices:
                  del df[column]
  
      df[target] = y.values
  
      return [df]
  
imports: from pandas.api.types import is_numeric_dtype
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.pipeline import Pipeline
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from autocorrect import Speller
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.ensemble import RandomForestRegressor
  from nylon.preprocessing.preprocessing import process_dates
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
label_desc: Use image node_id 6 for calling a global function with example usage: handle_importance(preprocess, df, target) and returns: list